What is AI in Supply Chain? A Comprehensive Guide

How is AI Being Used in the Supply Chain?

AI is now being used in every aspect of the supply chain, from planning and supplier sourcing to production, transportation, and logistics. In this section, we’ll elaborate on how AI works in these stages:

1. Planning

Planning in the supply chain is all about predicting possible needs and allocating resources needed to manufacture and deliver goods. It can involve anticipating customer demands, scheduling production, monitoring stock levels, and planning the storage and delivery of goods. In this stage, your company needs to identify and prepare for possible disruptions (e.g., port closures, warehouse damage, or production stoppages) in the supply chain.

With AI technologies, your company can make planning easier than ever before. Here’s where AI proves valuable:

Demand Forecasting

By analyzing different factors (like past sales data or market trends), AI helps predict future demand in real-time. This helps you determine the best inventory levels, production schedules, and delivery plans to meet even evolving customer demands effectively.

Example: Walmart, a US-based multinational retailer, employs AI to manage and anticipate possible changes in customer demands, especially during busy shopping periods like Black Friday. AI helps Walmart envision exactly what the scenario will be on those days and place inventory in the right place at the right time.

Risk Management

AI analyzes different data sources (e.g., historical data, weather patterns, market conditions, or even geopolitical events) to discover possible risks in your supply chain. It also offers real-time risk assessments, simulates scenarios, and proposes strategies to reduce risks.

Example: Beyond daily operations and demand forecasting, AI helps Walmart prepare for unexpected events like natural disasters. It quickly simulated these scenarios and suggested alternative ways to monitor the supply chain in real-time. To do so, Walmart’s AI/ML models will analyze so-called “future” data like macroweather patterns or macroeconomic trends. For this reason, when Hurricane Ian hit Southwest Florida in 2022 and damaged a Walmart distribution center, Walmart promptly reacted to this situation by rerouting shipments to fulfill customer demands in all stores.

Production Planning

Similar to the mentioned capabilities, AI helps schedule production and allocate resources effectively to avoid delays. Accordingly, it analyzes variables like production capacity, customer orders, and resource availability to plan production and ensure no resources (e.g., labor, equipment, or materials) are wasted.

2. Supplier Management

Supplier management is crucial to any company as it ensures your supply chain will operate effectively and in alignment with your quality and delivery standards. However, there’s a lot to do at this stage, from supplier selection and performance monitoring to relationship and contract management. That’s why AI comes into play to relieve burdens on human employees and reduce costs.

Below’s how AI can support supplier management:

New Supplier Selection

AI considers a wide range of factors like supplier performance, capabilities, risk profiles, and pricing. This offers you insightful recommendations to make informed decisions about who to partner with.

Example: Companies like Unilever or Siemens leverage AI to seek supply sources quickly. Particularly, the technology helps Unilever shorten a list of potential new suppliers by digging into suppliers’ information on websites (e.g., customer ratings, intellectual property data, or sustainability scorecards) to verify their international trading experiences. Meanwhile, AI helps Siemens seek alternative supply sources to deal with the shortage of Surlyn. Within days, AI listed 150 Surlyn distributors and prevented supply disruptions.

Supplier Management

AI can improve your supplier management and foster stronger partnerships. Particularly, it can help your business determine additional sourcing options within your existing supplier base. This not only increases flexibility for your company in re-cooperating with current suppliers, but also gives them new business opportunities.

Example: Koch Industries – one of the world’s largest conglomerate corporations – uses AI to evaluate whether its current supplier network can offer extra materials. This reduces the need to onboard new suppliers. By analyzing SKU-level data, AI can identify potential suppliers. It even can automatically generate and send new RFQs (Requests for Quotation) to the suppliers. This saves 60-90% of the time suppliers need to spend on RFQs, speeding up the procurement process.

Automated Negotiation

AI helps negotiate prices and purchasing terms with your suppliers.

Example: Maersk uses Pactum AI to automate negotiations. Particularly, the chatbot quickly searches for transportation rates in current contracts to negotiate with vendors. Even if it finds no rate in these agreements, it can automate the generation of new quotes based on relevant sources. This saves Maersk much time in offering timely and suitable pricing information.

Contract Analysis

AI also retrieves crucial information from agreements to create summaries that help simplify the review process. Moreover, it can detect possible risks within agreement terms to ensure compliance and avoid unexpected problems.

3. Production

AI is widely used in the manufacturing process across industries, from the design phase to product commercialization and equipment maintenance. With the assistance of AI, your company can speed up production and boost productivity while maintaining high-quality products.

Below’s how AI brings value to production in the supply chain:

Product Manufacturing

Manufacturers like Ford, BWM, or Tesla use AI-powered robots in manufacturing and assembling parts automatically. These autonomous robots help accelerate production and reduce costs while maintaining accuracy and high quality for products. Moreover, AI can help machinery identify, organize, and track products on conveyor belts by scanning QR codes, barcodes, or RFID (Radio Frequency Identification) tags.

Example: Ford leverages Javier – an autonomous mobile robot – to operate all 3D Carbon printers without human intervention. These printers can produce low-volume, custom parts, like a brake line bracket for the GT Performance Package of the Ford Mustang Shelby GT500. After the printed parts are finished, the printer will send a signal to Javier. Then, Javier will let the printer know it has arrived and is about to pick the parts up.

Quality Control

Companies now use AI-powered tools to inspect products on conveyor belts and spot real-time defects (e.g., incorrect assembly or cracks). This ensures consistent compliance with quality standards and only allows high-quality products to go through the manufacturing process.

Example: The BMW iFACTORY developed AIQX (Artificial Intelligence Quality Next) for automatic quality inspection. Particularly, AIQX can instantly analyze data recorded from camera systems and sensors in the conveyor belt to identify anomalies and check completeness in the assembly process. Its Acoustic Analytics functionality can automatically test audio-based quality by using microphones on the car seats to record driving noises for AI to analyze.

Predictive Maintenance

This is one of AI’s most popular applications in production. By using data collected from machines through sensors, AI systems can forecast which equipment is most likely to fail. This allows companies to adjust their maintenance schedules accordingly, tackle potential failures proactively, and avoid unexpected downtime. As a result, they not only expand the equipment’s lifespan but also minimize maintenance costs.

Example: Toyota’s Indiana branch uses AI to analyze real-time sensor data and current manufacturing data (e.g., work orders). This data gives the company in-depth insights into the overall health of its machinery and relevant components. For this reason, Toyota can detect any abnormal signs promptly and optimize maintenance work, improving uptime by 10-20% and reducing operational costs by 15-50%.

Material Discovery & Optimization

AI also helps manufacturers optimize current materials for better performance and explore new ones with enhanced properties. This can lead to more durable, sustainable, and efficient materials.

Example: Evonik Industries – a German chemical producer – collaborated with IBM to speed up material discovery and design. IBM’s AI algorithms help predict and fine-tune polymer properties, reducing the formulation problem from 10 or more years to months.

4. Delivery & Logistics

The final application of AI in supply chains is in overall logistics and warehouse operations. With AI, companies can store goods in an organized manner, track inventory levels easily, and transport goods to the right destination on time.

Here are some of what AI can do in logistics:

Warehouse Operations

Companies now use AI to optimize their warehouse operations. By automating repetitive tasks like order picking, packing, or inventory checks, they can accelerate warehouse operations while minimizing human errors and reducing costs. Further, AI can provide warehouse workers with optimal routes to pick up items, hence increasing overall flow. AI also analyzes warehouse layouts and recommends the best use of available space.

Example: Amazon has leveraged AI-powered robots like Sparrow, Robin, and Sequoia to load, stock, and pick up bulky goods safer and faster than human workers. The corporation even equips ML-programmed robotic arms to identify the shapes and sizes of packages. These arms then pick up and hand the parcels to small mobile robots (often called “shin-high bots”) for transporting to the next stop (e.g., sorting or packaging). Amazon even installs the VAPR (Vision-Assisted Package Retrieval) system. By reading barcodes and displaying a green/red mark on parcels, the system shows drivers which one they need to pick up for each delivery stop.

Last Mile Dynamic Route Optimization

This refers to the process of optimizing real-time delivery routes, particularly for the final leg of the delivery journey from a distribution center to a customer’s doorstep. Identifying last-mile routes is hard but crucial as it strongly affects delivery efficiency and customer satisfaction. With AI, companies can simplify this process by analyzing real-time data (e.g., traffic conditions or delivery priorities) to adjust routes.

Example: Aramex Australia – a multinational logistics company – collaborated with Amazon to create the Dynamic Delivery Planner (which is known as an AWS last mile routing technology). This system analyzes real-life data (e.g., delivery volume or travel time) to optimize delivery routes and distribute packages dynamically. As a result, Aramex Australia improved delivery efficiency by 29% and increased its driver experience.

The Current and Future State of AI in Supply Chain

With the mentioned applications, AI is becoming an indispensable part of supply chains. One research has shown that the global value of AI in this segment is expected to rise by 38.9% annually from 2024 to 2030, with a main focus on machine learning and computer vision.

Researchers suggested that the rise of eCommerce has fueled this growth. More and more customers go online to make purchases and they wish products to be delivered to their front door as quickly and reliably as possible. Therefore, various companies, especially in the automotive, manufacturing, and retail industries, are adopting AI and automation technologies to speed up their supply chains while maintaining production efficiency.

AI Innovations Enhancing Supply Chains

Along with this demand, some AI-powered innovations appear to improve supply chain operations. They include:

Generative Probabilistic Planning (GPP)

In 2024, Korean researchers introduced GPP, a new GenAI technique. As we all know, a supply chain is a large, complex network, and if any changes (e.g., customer demands or lead times) happen, companies have difficulties managing them all. GPP uses deep reinforcement learning and attention-based graph neural networks to learn from data and create supply action plans that can adapt to these changes. For instance, if a product demand suddenly increases or a delivery is postponed, GPP can modify the plan to keep everything seamless.

AI Towards Sustainability

The European Union has recently issued a series of ESG requirements for supply chains. ESG stands for Environmental, Social, and Governance. These laws include the Corporate Sustainability Reporting Directive, the European Sustainability Reporting Standards, and the Corporate Sustainability Due Diligence Directive. They require EU and non-EU companies to detect and reduce negative impacts on the environment and human rights in supply chains.

With AI, companies can ensure ESG compliance in their supply chain operations. Here are some of what AI can do:

calculate the most fuel-efficient routes to reduce carbon footprint;
optimize resource allocation and material usage to mitigate overstock or unnecessary production;
proactively adjust energy consumption to minimize the overall energy footprint;
discover potential human rights issues or labor violations;
analyze workplace safety data to forecast possible dangers and suggest preventive solutions;
manage external risks like environmental disruptions, market volatility, or geopolitical instability
and more!

Digital Twins

A digital twin is a virtual representation of your physical supply chain network. This computer model reflects your real-world operations and processes of delivering products from raw materials to end-users. It allows you to create different mock scenarios to test “what-if” hypotheses and optimize operations without compromising the physical supply chain.

Maersk, a global shipping and logistics company, developed AI-powered digital twins to track a container’s location and temperature, optimize terminal operations, and monitor maintenance workflows. The company also uses data from historical voyages, vessel reports, weather information, and more to simulate the digital twins of vessels for fuel and emission reduction.

What to Do Before Integrating AI into Supply Chain

While AI proves beneficial in supply chains, we can’t deny this technology itself also comes with several limitations. We all know AI works primarily with data. If the data is inaccurate, incomplete, or outdated, AI won’t function well. Further, various companies are worried about ethical or legal issues and lack of transparency associated with AI-generated decisions. Confronting these problems, they must have proper planning to integrate AI seamlessly into all supply chain stages and make effective use of this technology.

Below is what you can do if you want to maximize the benefits of AI usage in supply chains:

Step 1: Evaluate the Existing Supply Chain Network

By looking at the entire supply chain, your business can identify what’s working well and where problems (e.g., high costs or delays) constantly arise. Further, you need to process structured and unstructured data to ensure its accuracy, consistency, and completeness for effective analytics.

Step 2: Create a Roadmap

Decide which challenges to tackle first based on their impact on your supply chain network. In other words, prioritize the most critical and pressing problems to focus your main efforts and ensure better outcomes.

Step 3: Select or Develop the Right AI Solution

Evaluate different AI systems available to see which one matches your company’s overall goals and solves pain points. You can consider expert guidance or advice from AI specialists to select the best option for your supply chain network.

Step 4: Integrate and Implement the AI Solution

Work closely with your AI solution provider and internal IT team to ensure smooth integration and later implementation. Plus, be prepared for possible challenges during this stage, for example, the new AI solution doesn’t align with your existing infrastructure. Further, you should design a detailed plan for training employees on how to use the new system effectively. Don’t forget to keep communication open to make employees quickly adapt to AI technology.

Step 5: Monitor and Refine

Consistently test the AI system and track its performance. Make necessary adjustments to enhance its effectiveness. This regular monitoring keeps the AI system updated on any changes in your supply chain and running smoothly over time.

Improve Your Supply Chain with Designveloper’s AI Solutions

Having a well-structured strategy is crucial yet insufficient to help you apply AI to the supply chain effectively. Choosing the right partner is equally important. With extensive expertise and years of experience in software and AI development, Designveloper is committed to building robust, scalable AI solutions that align with your specific needs. We help you smoothly integrate AI into supply chain operations.

One of our successful projects is to help a leading electronics manufacturer reduce defects by 40% and lower inspection costs by 25% with the custom integration of AI. Further, we also provide end-to-end support, from planning to full deployment and ongoing maintenance, within your budget.

So, if you want to discuss your idea further with us, contact us now!